def generate_ma_table(frequency, start=None, end=None, ma_list=None, market_ticker='^GSPC'):
"""
:param frequency: ['w','d']
:param ma_list: ['SMA','EMA','DMA']:Period
:param start:
:param end:
:param :market_ticker:
:return:
"""
end = datetime.datetime.today() if end == None else end
start = end - datetime.timedelta(days=365) if start == None else start
if frequency == 'w':
max_period = 100 if ma_list==None else max(max(ma_list.values()))
timedelta = datetime.timedelta(weeks=max_period)
else:
max_period = 365 if ma_list==None else max(max(ma_list.values()))
timedelta = datetime.timedelta(days=max_period)
prices = pdr.get_data_yahoo(market_ticker, interval=frequency)['Close']
datetest = datetime.datetime(2019, 2, 12)
# print(prices)
# print(type(prices))
# print(prices.asof(datetest))
print(prices)
print(prices[prices.keys() == str(datetest)])
# print(prices.take())
pd.rolling(10)
def KeltnerChannel(df, n):
MA=pd.Series(pd.rolling(df['Close']).mean())
RangeMA = pd.Series(pd.rolling(df['High'] - df['Low']).mean())
Upper=MA + RangeMA * kelt_mult
Lower=MA - RangeMA * kelt_mult
df = df.join(Upper)
df = df.join(Lower)
return df
def smooth(df,columns,names,period,**keyword_parameters):
"""Smooths out spikey data in a 3D trajectory by running a moving average
over specified columns of input dataframe. Optionally, the smoothed curve
can be shifted back close to its originally position by including the shift
optional argument.
Args:
df (dataframe): pandas dataframe
columns (list): list of dataframe headers to smooth
names (list): names of new columns in same order as columns
Returns:
dataframe: pandas dataframe containing smoothed and/or shifted columns.
"""
df=deepcopy(df)
for i in range(len(columns)):
df[names[i]]=pd.rolling(df[columns[i]],period).mean()
if ('shift' in keyword_parameters):
shift = keyword_parameters['shift']
if shift:
shift_names=keyword_parameters['shift_names']
shift_period=keyword_parameters['shift_period']
for i in range(len(columns)):
df[shift_names[i]]=df[names[i]].shift(shift_period)
return df
def test_run2():
dates = pd.date_range('2010-01-01', '2012-12-31')
symbols = ['SPY', 'FB', 'GOOG', 'NFLX']
df = get_data(symbols, dates)
# PLot SPY data, retain matplotlib axis object
ax = df['SPY'].plot(title="SPY rolling mean", label='SPY')
# Compute rolling mean using 20 day window
rm_SPY = pd.rolling_mean(df['SPY'], window=20)
# Non deprecated version
pd.rolling(values, window=window).mean()
def get_rolling_std(values, window):
"""Return rolling standard deviation of given values, using specified window size."""
# TODO: Compute and return rolling standard deviation
return pd.rolling(values, window=window).std()
# ax=ax adds new plot to old plot
rm_SPY.plot(label='Rolling mean', ax=ax)
#labels
ax.add_xlabel("Date")
ax.add_ylabel("Price")
ax.legend(loc='upper left')
plt.show()
# Compute Bollinger Bands
# 1. Computer rolling mean
# 2. Compute rolling standard deviation
# 3. Computer upper_band and lower_band
# NON DEPRECATED
def get_rolling_mean(values, window):
"""Return rolling mean of given values, using specified window size."""
return pd.rolling(values, window=window).mean()
def get_rolling_std(values, window):
"""Return rolling standard deviation of given values, using specified window size."""
# TODO: Compute and return rolling standard deviation
return pd.rolling(values, window=window).std()
def get_bollinger_bands(rm, rstd):
"""Return upper and lower Bollinger Bands."""
# TODO: Compute upper_band and lower_band
upper_band = rm + 2 * rstd
lower_band = rm - 2 * rstd
return upper_band, lower_band
def OBV(df, n):
i = 0
OBV = [0]
while i < df.index[-1]:
if df.at(i + 1, 'Close') - df.at(i, 'Close') > 0:
OBV.append(df.at(i + 1, 'TradeVolume'))
if df.at(i + 1, 'Close') - df.at(i, 'Close') == 0:
OBV.append(0)
if df.at(i + 1, 'Close') - df.at(i, 'Close') < 0:
OBV.append(-df.at(i + 1, 'TradeVolume'))
i = i + 1
OBV = pd.Series(OBV)
OBV_ma = pd.Series(pd.rolling(OBV, n).mean(), name='OBV_' + str(n))
return OBV_ma
def zmld_so(s, t, p, threshold=0.05, smooth=None):
"""
Computes mixed layer depth of Southern Ocean waters.
Parameters
----------
s : array_like
salinity [psu (PSS-78)]
t : array_like
temperature [℃ (ITS-90)]
p : array_like
pressure [db].
smooth : int
size of running mean window, to smooth data.
References
----------
Mitchell B. G., Holm-Hansen, O., 1991. Observations of modeling of the
Antartic phytoplankton crop in relation to mixing depth. Deep Sea
Research, 38(89):981-1007. doi:10.1016/0198-0149(91)90093-U
"""
from pandas import rolling
sigma_t = sigmatheta(s, t, p)
depth = copy(p)
if smooth is not None:
sigma_t = rolling(sigma_t, smooth, min_periods=1).mean()
sublayer = np.where(depth[(depth >= 5) & (depth <= 10)])[0]
sigma_x = np.nanmean(sigma_t[sublayer])
nan_sigma = np.where(sigma_t < sigma_x + threshold)[0]
sigma_t[nan_sigma] = np.nan
der = np.divide(np.diff(sigma_t), np.diff(depth))
mld = np.where(der == np.nanmax(der))[0]
zmld = depth[mld]
return zmld
def zmld_so(s, t, p, threshold=0.05, smooth=None):
"""
Computes mixed layer depth of Southern Ocean waters.
Parameters
----------
s : array_like
salinity [psu (PSS-78)]
t : array_like
temperature [℃ (ITS-90)]
p : array_like
pressure [db].
smooth : int
size of running mean window, to smooth data.
References
----------
Mitchell B. G., Holm-Hansen, O., 1991. Observations of modeling of the
Antartic phytoplankton crop in relation to mixing depth. Deep Sea
Research, 38(89):981-1007. doi:10.1016/0198-0149(91)90093-U
"""
from pandas import rolling
sigma_t = sigmatheta(s, t, p)
depth = copy(p)
if smooth is not None:
sigma_t = rolling(sigma_t, smooth, min_periods=1).mean()
sublayer = np.where(depth[(depth >= 5) & (depth <= 10)])[0]
sigma_x = np.nanmean(sigma_t[sublayer])
nan_sigma = np.where(sigma_t < sigma_x + threshold)[0]
sigma_t[nan_sigma] = np.nan
der = np.divide(np.diff(sigma_t), np.diff(depth))
mld = np.where(der == np.nanmax(der))[0]
zmld = depth[mld]
return zmld
def CCI(df, n):
PP = (df['High'] + df['Low'] + df['Close']) / 3
CCI = pd.Series((PP - pd.rolling(PP, n).mean()) / pd.rolling(PP, n).std(),
name='CCI_' + str(n))
return CCI
def 변동성대비모멘텀(데이터):
return (데이터 / 데이터.shift(12)) / pd.rolling(12).std(데이터)
#!/usr/bin/python
# coding: UTF-8
import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import talib
df = ts.get_k_data('600600')
df['MA10_rolling'] = pd.rolling(df['close']).mean(10)
close = [float(x) for x in df['close']] # 调用talib计算10日移动平均线的值
df['MA10_talib'] = talib.MA(np.array(close), timeperiod=10)
df.tail(12)
def MA(df, n):
##MA = pd.Series(pd.rolling_mean(df['Close'], n), name = 'MA_' + str(n))
MA = pd.Series(pd.rolling(n).mean(), name='MA_' + str(n))
df = df.join(MA)
return df
close_px
close_px['AAPL'].plot()
close_px.ix['2009'].plot()
close_px['AAPL'].ix['01-2011':'03-2011'].plot()
appl_q = close_px['AAPL'].resample('Q-DEC',fill_method='ffill')
appl_q.ix['2009'].plot()
close()
close()
appl_q.ix['2009'].plot()
close()
appl_q = close_px['AAPL'].resample('Q-DEC',fill_method='ffill')
close_px['AAPL'].ix['01-2011':'03-2011'].plot()
close()
close_px.AAPL.plot()
pd.rolling_mean(close_px.AAPL,250).plot()
pd.rolling(close_px.AAPL,250).mean().plot()
pd.rolling_mean(close_px.AAPL,250).plot()
appl_std250 = pd.rolling_std(close_px.AAPL,250,min_periods=10)
appl_std250 = pd.rolling(close_px.AAPL,250,min_periods=10).std()
appl_std250 = close_px.AAPL.rolling(250,min_periods=10).std()
appl_std250[5:12]
appl_std250.plot()
close()
appl_std250.plot()
expanding_mean = lambda x:rolling_mean(x, len(x), min_periods=1)
close_px.rolling(60).mean().plot(logy=True)
close()
fig_axes = plt.subplots(nrows=2,ncols=1,sharex=True,sharey=True,figsize=(12,7))
aapl_px = close_px.AAPL['2005':'2009']
ma60 = aapl_px.rolling(60,min_periods=50)
ewma60 = pd.ewma(aapl_px,span=60)
def get_rolling_std(values, window):
"""Return rolling standard deviation of given values, using specified window size."""
# TODO: Compute and return rolling standard deviation
return pd.rolling(values, window=window).std()
def get_rolling_mean(values, window):
"""Return rolling mean of given values, using specified window size."""
return pd.rolling(values, window=window).mean()